{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "Chapter 10\n",
    "\n",
    "Copyright 2017 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim.py module\n",
    "from modsim import *\n",
    "\n",
    "from pandas import read_html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Under the hood\n",
    "\n",
    "To get a `DataFrame` and a `Series`, I'll read the world population data and select a column.\n",
    "\n",
    "`DataFrame` and `Series` contain a variable called `shape` that indicates the number of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 11)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'data/World_population_estimates.html'\n",
    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
    "table2 = tables[2]\n",
    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
    "                  'thomlinson', 'durand', 'clark']\n",
    "table2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census = table2.census / 1e9\n",
    "census.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "un = table2.un / 1e9\n",
    "un.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DataFrame` contains `index`, which labels the rows.  It is an `Int64Index`, which is similar to a NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
       "            1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
       "            1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "            1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
       "            1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
       "            2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
       "            2016],\n",
       "           dtype='int64', name='Year')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And `columns`, which labels the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['census', 'prb', 'un', 'maddison', 'hyde', 'tanton', 'biraben', 'mj',\n",
       "       'thomlinson', 'durand', 'clark'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And `values`, which is an array of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "       [6788214394, 6809972000.0, 6834721933.0, 6764086000.0, nan, nan,\n",
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       "       [6858584755, 6892319000.0, 6916183482.0, nan, nan, nan, nan, nan,\n",
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       "        nan, nan, nan],\n",
       "       [7013871313, 7057075000.0, 7080072417.0, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan],\n",
       "       [7092128094, 7136796000.0, 7162119434.0, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan],\n",
       "       [7169968185, 7238184000.0, 7243784000.0, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan],\n",
       "       [7247892788, 7336435000.0, 7349472000.0, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan],\n",
       "       [7325996709, 7418151841.0, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Series` does not have `columns`, but it does have `name`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'census'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It contains `values`, which is an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.55762865, 2.59493988, 2.63677231, 2.68205339, 2.7302281 ,\n",
       "       2.78209894, 2.83529967, 2.89134972, 2.94813725, 3.00071659,\n",
       "       3.04300151, 3.08396693, 3.14009322, 3.20982788, 3.28120131,\n",
       "       3.35042579, 3.42067792, 3.49033371, 3.56231382, 3.63715905,\n",
       "       3.71269774, 3.79032695, 3.86656865, 3.94209644, 4.01660881,\n",
       "       4.08908323, 4.16018501, 4.23208458, 4.30410575, 4.37901394,\n",
       "       4.45136274, 4.53441012, 4.61456656, 4.69573674, 4.77456939,\n",
       "       4.8564627 , 4.94057123, 5.02720049, 5.11455717, 5.20144011,\n",
       "       5.28895593, 5.37158592, 5.45613628, 5.53826832, 5.61868213,\n",
       "       5.69920299, 5.77944059, 5.85797254, 5.93521325, 6.01207492,\n",
       "       6.08857138, 6.16521925, 6.24201635, 6.31859096, 6.39569951,\n",
       "       6.47304473, 6.55126353, 6.62991376, 6.70904978, 6.78821439,\n",
       "       6.85858475, 6.93599949, 7.01387131, 7.09212809, 7.16996819,\n",
       "       7.24789279, 7.32599671])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And it contains `index`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
       "            1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
       "            1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "            1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
       "            1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
       "            2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
       "            2016],\n",
       "           dtype='int64', name='Year')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you ever wonder what kind of object a variable refers to, you can use the `type` function.  The result indicates what type the object is, and the module where that type is defined.\n",
    "\n",
    "`DataFrame`, `Int64Index`, `Index`, and `Series` are defined by Pandas.\n",
    "\n",
    "`ndarray` is defined by NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(table2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.numeric.Int64Index"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(table2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(table2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(table2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.numeric.Int64Index"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(census.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(census.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional exercise\n",
    "\n",
    "The following exercise provides a chance to practice what you have learned so far, and maybe develop a different growth model.  If you feel comfortable with what we have done so far, you might want to give it a try.\n",
    "\n",
    "**Optional Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'data/World_population_estimates.html'\n",
    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
    "len(tables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select `tables[1]`, which is the second table on the page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
       "      <th>Maddison (2008)[17]</th>\n",
       "      <th>HYDE (2010)[citation needed]</th>\n",
       "      <th>Tanton (1994)[18]</th>\n",
       "      <th>Biraben (1980)[19]</th>\n",
       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
       "      <th>Thomlinson (1975)[21]</th>\n",
       "      <th>Durand (1974)[22]</th>\n",
       "      <th>Clark (1967)[23]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-10000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2M[24]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1–10M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-9000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-8000</th>\n",
       "      <td>5.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5–10M</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-7000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-6000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population Reference Bureau (1973–2016)[15]  \\\n",
       "Year                                                 \n",
       "-10000                                         NaN   \n",
       "-9000                                          NaN   \n",
       "-8000                                           5.   \n",
       "-7000                                          NaN   \n",
       "-6000                                          NaN   \n",
       "\n",
       "        United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
       "Year                                                                          \n",
       "-10000                                                NaN                     \n",
       "-9000                                                 NaN                     \n",
       "-8000                                                 NaN                     \n",
       "-7000                                                 NaN                     \n",
       "-6000                                                 NaN                     \n",
       "\n",
       "       Maddison (2008)[17] HYDE (2010)[citation needed]  Tanton (1994)[18]  \\\n",
       "Year                                                                         \n",
       "-10000                 NaN                       2M[24]                NaN   \n",
       "-9000                  NaN                           4.                NaN   \n",
       "-8000                  NaN                           5.                NaN   \n",
       "-7000                  NaN                           8.                NaN   \n",
       "-6000                  NaN                          11.                NaN   \n",
       "\n",
       "        Biraben (1980)[19]  McEvedy & Jones (1978)[20] Thomlinson (1975)[21]  \\\n",
       "Year                                                                           \n",
       "-10000                 NaN                         4.0                 1–10M   \n",
       "-9000                  NaN                         NaN                   NaN   \n",
       "-8000                  NaN                         NaN                   NaN   \n",
       "-7000                  NaN                         NaN                   NaN   \n",
       "-6000                  NaN                         NaN                   NaN   \n",
       "\n",
       "       Durand (1974)[22] Clark (1967)[23]  \n",
       "Year                                       \n",
       "-10000               NaN              NaN  \n",
       "-9000                NaN              NaN  \n",
       "-8000              5–10M              NaN  \n",
       "-7000                NaN              NaN  \n",
       "-6000                NaN              NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1 = tables[1]\n",
    "table1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not all agencies and researchers provided estimates for the same dates.  Again `NaN` is the special value that indicates missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
       "      <th>Maddison (2008)[17]</th>\n",
       "      <th>HYDE (2010)[citation needed]</th>\n",
       "      <th>Tanton (1994)[18]</th>\n",
       "      <th>Biraben (1980)[19]</th>\n",
       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
       "      <th>Thomlinson (1975)[21]</th>\n",
       "      <th>Durand (1974)[22]</th>\n",
       "      <th>Clark (1967)[23]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1793.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>1863.</td>\n",
       "      <td>1912.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1968.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2070.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2092.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2145.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>2299.</td>\n",
       "      <td>2307.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2340.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Population Reference Bureau (1973–2016)[15]  \\\n",
       "Year                                               \n",
       "1913                                         NaN   \n",
       "1920                                         NaN   \n",
       "1925                                         NaN   \n",
       "1930                                         NaN   \n",
       "1940                                         NaN   \n",
       "\n",
       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
       "Year                                                                        \n",
       "1913                                                NaN                     \n",
       "1920                                             1860.0                     \n",
       "1925                                                NaN                     \n",
       "1930                                             2070.0                     \n",
       "1940                                             2300.0                     \n",
       "\n",
       "     Maddison (2008)[17] HYDE (2010)[citation needed]  Tanton (1994)[18]  \\\n",
       "Year                                                                       \n",
       "1913               1793.                          NaN                NaN   \n",
       "1920               1863.                        1912.                NaN   \n",
       "1925                 NaN                          NaN                NaN   \n",
       "1930                 NaN                        2092.                NaN   \n",
       "1940               2299.                        2307.                NaN   \n",
       "\n",
       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20] Thomlinson (1975)[21]  \\\n",
       "Year                                                                         \n",
       "1913                 NaN                         NaN                   NaN   \n",
       "1920                 NaN                         NaN                   NaN   \n",
       "1925                 NaN                      2000.0                   NaN   \n",
       "1930                 NaN                         NaN                   NaN   \n",
       "1940                 NaN                         NaN                   NaN   \n",
       "\n",
       "     Durand (1974)[22] Clark (1967)[23]  \n",
       "Year                                     \n",
       "1913               NaN              NaN  \n",
       "1920               NaN            1968.  \n",
       "1925               NaN              NaN  \n",
       "1930               NaN            2145.  \n",
       "1940               NaN            2340.  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we'll replace the long column names with more convenient abbreviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "table1.columns = ['PRB', 'UN', 'Maddison', 'HYDE', 'Tanton', \n",
    "                  'Biraben', 'McEvedy & Jones', 'Thomlinson', 'Durand', 'Clark']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the estimates are in a form Pandas doesn't recognize as numbers, but we can coerce them to be numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in table1.columns:\n",
    "    table1[col] = pd.to_numeric(table1[col], errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the results.  Notice that we are working in millions now, not billions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table1.plot()\n",
    "decorate(xlim=[-10000, 2000], xlabel='Year', \n",
    "         ylabel='World population (millions)',\n",
    "         title='Prehistoric population estimates')\n",
    "plt.legend(fontsize='small');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `xlim` to zoom in on everything after Year 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table1.plot()\n",
    "decorate(xlim=[0, 2000], xlabel='Year', \n",
    "         ylabel='World population (millions)',\n",
    "         title='CE population estimates')\n",
    "plt.legend(fontsize='small');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See if you can find a model that fits these data well from Year 0 to 1950.\n",
    "\n",
    "How well does your best model predict actual population growth from 1950 to the present?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# The function I found that best matches the data has the form\n",
    "# a + b / (c - x)\n",
    "\n",
    "# This function is hard to explain physically; that is, it doesn't\n",
    "# correspond to a growth model that makes sense in terms of human behavior.\n",
    "\n",
    "# And it implies that the population goes to infinity in 2040.\n",
    "\n",
    "xs = linspace(100, 1950)\n",
    "ys = 110 + 200000 / (2040 - xs)\n",
    "table1.plot()\n",
    "plot(xs, ys, color='gray', label='model')\n",
    "\n",
    "decorate(xlim=[0, 2000], xlabel='Year', \n",
    "         ylabel='World population (millions)',\n",
    "         title='CE population estimates')\n",
    "plt.legend(fontsize='small');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# And it doesn't do a particularly good job of predicting\n",
    "# actual growth from 1940 to the present.\n",
    "\n",
    "plot(census, ':', label='US Census')\n",
    "plot(un, '--', label='UN DESA')\n",
    "\n",
    "xs = linspace(1940, 2020)\n",
    "ys = 110 + 200000 / (2040 - xs)\n",
    "plot(xs, ys/1000, color='gray', label='model')\n",
    "\n",
    "decorate(xlim=[1950, 2016], xlabel='Year', \n",
    "         ylabel='World population (billions)',\n",
    "         title='Prehistoric population estimates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
